Total n = 34

Exclude subject 14: squeezed emergency ball while in the scanner. Exclude subject 30: subject wanted to discontinue after round 7 because of discomfort caused by glasses Exclude subject 34: the participant perceived the peripheral nerve stimulation to be uncomfortable, so stopped the scan after Round 2.

n = 31

QA

Prescan

## `summarise()` has grouped output by 'sub', 'nquestion', 'round_text'. You can override using the `.groups` argument.

## `summarise()` has grouped output by 'sub'. You can override using the `.groups` argument.

## Joining, by = "sub"
## Joining, by = "sub"

Prescan Performance
nquestion	correct¹	conf	conf_correct²
06
1	0.125	0.417	0.250
2	0.375	0.417	0.250
3	0.750	0.417	0.250
07
1	0.250	0.625	0.583
2	0.750	0.625	0.583
3	1.000	0.625	0.583
08
1	0.500	0.667	0.667
2	1.000	0.667	0.667
3	1.000	0.667	0.667
09
1	0.500	0.458	0.417
2	0.750	0.458	0.417
3	0.875	0.458	0.417
10
1	0.375	0.542	0.458
2	0.750	0.542	0.458
3	0.875	0.542	0.458
11
1	0.625	0.583	0.583
2	0.875	0.583	0.583
3	1.000	0.583	0.583
12
1	0.125	0.708	0.583
2	0.625	0.708	0.583
3	1.000	0.708	0.583
13
1	0.000	0.375	0.333
2	0.500	0.375	0.333
3	0.750	0.375	0.333
15
1	0.250	0.500	0.500
2	0.625	0.500	0.500
3	1.000	0.500	0.500
16
1	0.250	0.333	0.333
2	0.750	0.333	0.333
3	0.875	0.333	0.333
17
1	0.500	0.625	0.625
2	0.750	0.625	0.625
3	1.000	0.625	0.625
18
1	0.500	0.625	0.625
2	0.875	0.625	0.625
3	1.000	0.625	0.625
19
1	0.375	0.458	0.458
2	0.625	0.458	0.458
3	1.000	0.458	0.458
20
1	0.250	0.583	0.542
2	0.625	0.583	0.542
3	1.000	0.583	0.542
21
1	0.500	0.500	0.500
2	0.875	0.500	0.500
3	1.000	0.500	0.500
22
1	0.375	0.708	0.625
2	0.875	0.708	0.625
3	1.000	0.708	0.625
23
1	0.125	0.125	0.125
2	0.375	0.125	0.125
3	0.500	0.125	0.125
24
1	0.375	0.292	0.250
2	0.750	0.292	0.250
3	0.625	0.292	0.250
25
1	0.250	0.583	0.500
2	0.625	0.583	0.500
3	1.000	0.583	0.500
26
1	0.500	0.625	0.625
2	0.875	0.625	0.625
3	1.000	0.625	0.625
27
1	0.625	0.583	0.542
2	0.750	0.583	0.542
3	1.000	0.583	0.542
28
1	0.500	0.625	0.542
2	0.875	0.625	0.542
3	0.875	0.625	0.542
29
1	0.750	0.458	0.458
2	0.625	0.458	0.458
3	1.000	0.458	0.458
31
1	0.625	0.292	0.167
2	0.375	0.292	0.167
3	0.625	0.292	0.167
32
1	0.625	0.750	0.708
2	1.000	0.750	0.708
3	1.000	0.750	0.708
33
1	0.125	0.458	0.417
2	0.625	0.458	0.417
3	0.875	0.458	0.417
35
1	0.375	0.375	0.250
2	0.375	0.375	0.250
3	0.875	0.375	0.250
36
1	0.375	0.500	0.417
2	0.875	0.500	0.417
3	0.875	0.500	0.417
37
1	0.375	0.667	0.583
2	0.625	0.667	0.583
3	1.000	0.667	0.583
38
1	0.500	0.708	0.667
2	1.000	0.708	0.667
3	1.000	0.708	0.667
39
1	0.375	0.542	0.375
2	0.500	0.542	0.375
3	0.875	0.542	0.375
¹ accuracy < 0.5 are highlighted ² confidence correct < 0.25 are highlighted

Scan

Scan Performance
sub	correct¹	conf	conf_correct²
06	0.950	0.750	0.750
07	0.950	0.825	0.800
08	0.925	0.900	0.900
09	0.800	0.375	0.375
10	0.725	0.475	0.475
11	0.925	0.900	0.875
12	0.725	0.750	0.725
13	0.350	0.275	0.250
15	0.975	0.800	0.775
16	0.825	0.600	0.575
17	0.950	0.950	0.950
18	0.975	0.950	0.925
19	0.800	0.600	0.575
20	0.750	0.700	0.650
21	0.675	0.200	0.200
22	0.800	0.850	0.750
23	0.750	0.350	0.350
24	0.650	0.300	0.300
25	0.850	0.825	0.750
26	0.875	0.800	0.775
27	0.700	0.325	0.325
28	0.950	0.925	0.925
29	0.850	0.575	0.575
31	0.575	0.700	0.500
32	0.925	0.875	0.875
33	0.725	0.425	0.400
35	0.850	0.600	0.550
36	0.650	0.475	0.475
37	0.900	0.825	0.800
38	0.950	0.825	0.825
39	0.925	0.625	0.625
¹ accuracy < 0.5 are highlighted ² confidence correct < 0.25 are highlighted

Posttest1

## `summarise()` has grouped output by 'sub'. You can override using the `.groups` argument.

Postscan1 Performance
confidence range from 0 - 2.
npic	m¹	conf
06
30	0.875	1.500
45	1.000	1.875
60	1.000	1.875
75	1.000	2.000
07
30	0.500	1.250
45	0.875	1.750
60	1.000	2.000
75	1.000	2.000
08
30	0.500	1.125
45	1.000	2.000
60	1.000	2.000
75	1.000	2.000
09
30	0.000	0.250
45	0.500	0.875
60	1.000	1.750
75	1.000	2.000
10
30	0.500	0.625
45	0.500	1.000
60	0.625	1.125
75	1.000	2.000
11
30	1.000	1.750
45	1.000	2.000
60	1.000	2.000
75	1.000	2.000
12
30	0.500	1.250
45	0.875	1.625
60	0.875	1.750
75	1.000	1.750
13
30	0.000	0.000
45	0.375	0.500
60	0.500	1.250
75	0.750	1.625
15
30	0.625	1.250
45	1.000	2.000
60	1.000	2.000
75	1.000	2.000
16
30	0.625	0.750
45	1.000	1.875
60	1.000	2.000
75	1.000	2.000
17
30	1.000	2.000
45	1.000	2.000
60	1.000	2.000
75	1.000	2.000
18
30	0.500	0.750
45	1.000	2.000
60	1.000	2.000
75	1.000	2.000
19
30	0.250	1.000
45	1.000	2.000
60	1.000	2.000
75	1.000	2.000
20
30	0.500	2.000
45	0.875	2.000
60	0.875	2.000
75	0.875	2.000
21
30	0.000	0.000
45	0.500	1.000
60	1.000	1.500
75	1.000	2.000
22
30	0.625	1.125
45	1.000	2.000
60	0.875	1.750
75	1.000	2.000
23
30	0.125	0.125
45	0.250	0.250
60	0.375	0.500
75	0.500	0.875
24
30	0.125	0.625
45	0.500	1.125
60	0.750	1.625
75	0.875	2.000
25
30	0.500	1.000
45	1.000	2.000
60	1.000	2.000
75	1.000	2.000
26
30	0.500	0.875
45	1.000	2.000
60	1.000	2.000
75	1.000	2.000
27
30	0.125	0.125
45	0.875	1.375
60	0.875	1.375
75	0.875	1.375
28
30	0.625	1.125
45	1.000	2.000
60	1.000	2.000
75	1.000	2.000
29
30	0.125	0.250
45	0.625	1.375
60	1.000	2.000
75	1.000	2.000
31
30	0.125	0.125
45	0.500	1.125
60	1.000	1.750
75	1.000	2.000
32
30	0.875	1.625
45	1.000	2.000
60	1.000	2.000
75	1.000	2.000
33
30	0.000	0.000
45	0.500	1.000
60	1.000	1.625
75	1.000	2.000
35
30	0.875	1.750
45	1.000	2.000
60	1.000	2.000
75	1.000	2.000
36
30	0.875	1.375
45	1.000	2.000
60	1.000	2.000
75	1.000	2.000
37
30	0.500	1.000
45	1.000	2.000
60	1.000	2.000
75	1.000	2.000
38
30	0.500	1.250
45	1.000	2.000
60	1.000	2.000
75	1.000	2.000
39
30	0.000	0.000
45	0.500	1.000
60	0.500	1.000
75	1.000	2.000
¹ accuracy <= 0.5 are highlighted

Posttest2

Post scan 2 mean accuracy per subject
sub	m¹	m_npic²
06	0.7500	32.40000
07	1.0000	31.81250
08	0.8750	32.37500
09	1.0000	51.31250
10	1.0000	49.06250
11	1.0000	29.12500
12	0.8125	34.64286
13	0.8750	53.25000
15	1.0000	32.25000
16	1.0000	42.81250
17	1.0000	29.12500
18	0.9375	34.31250
19	1.0000	42.50000
20	0.7500	30.25000
21	1.0000	50.62500
22	1.0000	34.43750
23	1.0000	80.37500
24	1.0000	46.31250
25	0.9375	35.93750
26	0.9375	34.81250
27	1.0000	60.12500
28	1.0000	34.12500
29	0.9375	50.87500
31	1.0000	49.93750
32	0.9375	34.87500
33	0.7500	49.50000
35	1.0000	30.06250
36	0.8750	33.37500
37	1.0000	33.06250
38	0.9375	31.06250
39	0.9375	54.06250
¹ accuracy <= 0.5 are highlighted ² average img index > 75 are highlighted

Prescan analysis

Participant were instructed to answer the expected destination for 3 times during the route: once at Same, once at Overlapping, and once at non-overlapping. They also indicated their confidence towards the choice (sure vs. unsure).

Figures

## `summarise()` has grouped output by 'nquestion'. You can override using the `.groups` argument.

Stats

ANVOA for Accuracy:

## $ANOVA
##            Effect DFn DFd            F            p p<.05         ges
## 1           round   1  30   3.41437177 7.451247e-02       0.032781806
## 2       nquestion   2  60 137.16873890 4.158969e-23     * 0.622539299
## 3 round:nquestion   2  60   0.01125704 9.888082e-01       0.000128123

ANVOA for Confidence:

## $ANOVA
##            Effect DFn DFd            F            p p<.05          ges
## 1           round   1  30   0.00872093 9.262179e-01       6.692992e-05
## 2       nquestion   2  60 208.74353289 9.455601e-28     * 7.908325e-01
## 3 round:nquestion   2  60   2.99822590 5.740106e-02       2.212331e-02

ANVOA for high confidence accuracy:

## $ANOVA
##            Effect DFn DFd           F            p p<.05         ges
## 1           round   1  30   0.6639566 4.215883e-01       0.003342656
## 2       nquestion   2  60 200.1484829 2.840496e-27     * 0.809308761
## 3 round:nquestion   2  60   4.7775629 1.187581e-02     * 0.032706568

t-test for mean:

## 
##  Paired t-test
## 
## data:  sub_plot %>% filter(round == 1 & nquestion == 1) %>% .$m and sub_plot %>% filter(round == 2 & nquestion == 1) %>% .$m
## t = -1.1137, df = 30, p-value = 0.2743
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.18282487  0.05379262
## sample estimates:
## mean of the differences 
##             -0.06451613

## 
##  Paired t-test
## 
## data:  sub_plot %>% filter(round == 1 & nquestion == 2) %>% .$m and sub_plot %>% filter(round == 2 & nquestion == 2) %>% .$m
## t = -1.07, df = 30, p-value = 0.2932
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.16420077  0.05129754
## sample estimates:
## mean of the differences 
##             -0.05645161

## 
##  Paired t-test
## 
## data:  sub_plot %>% filter(round == 1 & nquestion == 3) %>% .$m and sub_plot %>% filter(round == 2 & nquestion == 3) %>% .$m
## t = -1.9715, df = 30, p-value = 0.05796
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.131349727  0.002317469
## sample estimates:
## mean of the differences 
##             -0.06451613

t-test for confidence:

## 
##  Paired t-test
## 
## data:  sub_plot %>% filter(round == 1 & nquestion == 1) %>% .$conf and sub_plot %>% filter(round == 2 & nquestion == 1) %>% .$conf
## t = 0.72468, df = 30, p-value = 0.4743
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.05865043  0.12316656
## sample estimates:
## mean of the differences 
##              0.03225806

## 
##  Paired t-test
## 
## data:  sub_plot %>% filter(round == 1 & nquestion == 2) %>% .$conf and sub_plot %>% filter(round == 2 & nquestion == 2) %>% .$conf
## t = 0.77948, df = 30, p-value = 0.4418
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.0522591  0.1167752
## sample estimates:
## mean of the differences 
##              0.03225806

## 
##  Paired t-test
## 
## data:  sub_plot %>% filter(round == 1 & nquestion == 3) %>% .$conf and sub_plot %>% filter(round == 2 & nquestion == 3) %>% .$conf
## t = -2.0647, df = 30, p-value = 0.04768
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.1443714456 -0.0007898447
## sample estimates:
## mean of the differences 
##             -0.07258065

t-test for high confidence accuracy:

## 
##  Paired t-test
## 
## data:  sub_plot %>% filter(round == 1 & nquestion == 1) %>% .$cor_conf and sub_plot %>% filter(round == 2 & nquestion == 1) %>% .$cor_conf
## t = 1.9853, df = 30, p-value = 0.05631
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.001387374  0.098161567
## sample estimates:
## mean of the differences 
##               0.0483871

## 
##  Paired t-test
## 
## data:  sub_plot %>% filter(round == 1 & nquestion == 2) %>% .$cor_conf and sub_plot %>% filter(round == 2 & nquestion == 2) %>% .$cor_conf
## t = -0.19687, df = 30, p-value = 0.8453
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.09172143  0.07559240
## sample estimates:
## mean of the differences 
##            -0.008064516

## 
##  Paired t-test
## 
## data:  sub_plot %>% filter(round == 1 & nquestion == 3) %>% .$cor_conf and sub_plot %>% filter(round == 2 & nquestion == 3) %>% .$cor_conf
## t = -2.4427, df = 30, p-value = 0.02068
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.17768390 -0.01586448
## sample estimates:
## mean of the differences 
##             -0.09677419

Scan analysis

Early vs. Late stop accuracy during scan:

Accuracy per round:

## `summarise()` has grouped output by 'round'. You can override using the `.groups` argument.

Distribution of picture index:

Grouped in 10:

## `summarise()` has grouped output by 'npic_10', 'route'. You can override using the `.groups` argument.

Grouped in 5:

## `summarise()` has grouped output by 'npic_5', 'route'. You can override using the `.groups` argument.

Every picture:

Posttest analysis

Posttest 1

## `summarise()` has grouped output by 'sub'. You can override using the `.groups` argument.

Posttest 2

Average accuracy = 0.9435484

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Preliminary behavioral data

Wanjia Guo

January 25, 2023